'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Any				=["image_processor", "tokenizer"]
     lowerCamelCase    :	str				="LayoutLMv3ImageProcessor"
     lowerCamelCase    :	List[str]				=("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






     def __init__(					self						:						int    ,							a						:						int=None    ,							a						:						Dict=None    ,							**a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''    ,							a    ,							)
             __lowerCamelCase            =    kwargs.pop('''feature_extractor'''       )

         __lowerCamelCase            =    image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a    ,							a       )






     def __call__(					self						:						Union[str, Any]    ,							a						:						Optional[Any]    ,							a						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,							a						:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,							a						:						Union[List[List[int]], List[List[List[int]]]] = None    ,							a						:						Optional[Union[List[int], List[List[int]]]] = None    ,							a						:						bool = True    ,							a						:						Union[bool, str, PaddingStrategy] = False    ,							a						:						Union[bool, str, TruncationStrategy] = None    ,							a						:						Optional[int] = None    ,							a						:						int = 0    ,							a						:						Optional[int] = None    ,							a						:						Optional[bool] = None    ,							a						:						Optional[bool] = None    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = True    ,							a						:						Optional[Union[str, TensorType]] = None    ,							**a						:						List[Any]    ,							):





         """simple docstring"""



         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         # first, apply the image processor
         __lowerCamelCase            =    self.image_processor(images=a    ,							return_tensors=a       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(a    ,							a       ):
                 __lowerCamelCase            =    [text]  # add batch dimension (as the image processor always adds a batch dimension)
             __lowerCamelCase            =    features['''words''']

         __lowerCamelCase            =    self.tokenizer(
             text=text if text is not None else features['''words''']    ,							text_pair=text_pair if text_pair is not None else None    ,							boxes=boxes if boxes is not None else features['''boxes''']    ,							word_labels=a    ,							add_special_tokens=a    ,							padding=a    ,							truncation=a    ,							max_length=a    ,							stride=a    ,							pad_to_multiple_of=a    ,							return_token_type_ids=a    ,							return_attention_mask=a    ,							return_overflowing_tokens=a    ,							return_special_tokens_mask=a    ,							return_offsets_mapping=a    ,							return_length=a    ,							verbose=a    ,							return_tensors=a    ,							**a    ,							)

         # add pixel values
         __lowerCamelCase            =    features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
             __lowerCamelCase            =    self.get_overflowing_images(a    ,							encoded_inputs['''overflow_to_sample_mapping''']       )
         __lowerCamelCase            =    images

         return encoded_inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						str    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]       )

         if len(a       ) != len(a       ):
             raise ValueError(
                 '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                 f""" {len(a       )} and {len(a       )}"""       )

         return images_with_overflow






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							*a						:						str    ,							**a						:						Dict       ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a    ,							**a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							*a						:						Optional[int]    ,							**a						:						Union[str, Any]       ):





         """simple docstring"""



         return self.tokenizer.decode(*a    ,							**a       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         return ["input_ids", "bbox", "attention_mask", "pixel_values"]






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,							a    ,							)
         return self.image_processor_class






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,							a    ,							)
         return self.image_processor

'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__UpperCAmelCase					=object()

# For specifying empty leaf dict `{}`
__UpperCAmelCase					=object()






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        __lowerCamelCase            =    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__							,    ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[int]:
    def replace(UpperCamelCase__							,    UpperCamelCase__							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__							,    UpperCamelCase__							):
                return replacement
        return val

    return replace






def __lowerCAmelCase					(				)      ->       Dict:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''							,    UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''							,    UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__							,    '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''							,    UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__							,    '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''							,    UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    _get_partition_rules()
    __lowerCamelCase            =    _replacement_rules(UpperCamelCase__							)
    __lowerCamelCase            =    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    __lowerCamelCase            =    {k: replace(UpperCamelCase__							,    UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)
